Related Experiment Video
Updated: Jan 31, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data
Tatsunori Osone1, Tomoka Takao1, Shigeo Otake1
1Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.
Abstract:
Weighted gene co-expression network analysis (WGCNA) is among the most widely employed methods in bioinformatics. WGCNA enables the identification of gene clusters (modules) exhibiting correlated expression patterns, the association of these modules with traits, and the exploration of candidate biomarker genes by focusing on hub genes within the modules. WGCNA has been successfully applied in diverse biological contexts. However, conventional algorithms manifest three principal limitations: the assumption of scale-free topology, the requirement for parameter tuning, and the neglect of regression line slopes. These limitations are addressed by SGCRNA. SGCRNA provides Julia functions for the analysis of co-expression networks derived from various types of biological data, such as gene expression data. The Julia packages and their source code are freely available at https://github.com/C37H41N2O6/SGCRNAs.jl.
Related Concept Videos
pH Scale
Constraints and Statical Determinacy
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Analysis of Population Pharmacokinetic Data
Chromatin Position Affects Gene Expression
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
Overview of Microsoft Excel as a Data Analysis Tool

